The subject matter described in this specifically relates generally to unmanned aerial vehicles (UAVs), e.g., UAVs configured to determine crop height and to follow rows.
The use of UAVs in agriculture is an active research topic. Existing work mostly utilizes UAVs to deliver aerial imagery of fields in a more timely and lower cost manner than traditional methods, such as manned aircraft and satellite imagery. Using a large UAV, it is possible to classify different vegetation in a field. Differentiating between vegetation types is used for weed management practices and coordinating ground robots. Small rotorcrafts operating at altitudes of 200 m above the ground and speeds of 30 km/h are capable of surveying up to 70 ha/hr. A smaller UAV operating at ranges of 10 m above the ground is capable of surveying rice paddies with a multi-spectral camera. By operating close to the crops, the impact of atmospheric distortion is reduced, but fast and accurate altitude and position measurements are needed.
In forestry applications there has been a significant amount of research using 3D LiDARs to measure canopy cover, biomass, and tree heights. Tree heights have been assessed using man-portable LiDAR systems, collecting data similar to what we desire to collect for corn crops. This system is cumbersome as it requires scientists to reposition the system at all data collection points.
LiDARs have been used in conjunction with aerial platforms for forestry experiments as well. LiDARs generally require larger platforms that are difficult and risky to operate close to crops, which means they are forced to fly at high altitudes where the irregularity of the tree shapes makes feature extraction difficult. These conditions also push LiDARs outside their recommended operating specifications. UAVs can mitigate these problems by flying at altitudes between 10-40 m, which produces information with a higher spatial density. At these altitudes, a heavy and expensive LiDAR is needed to achieve a high spatial information density.
Simultaneous localization and mapping (SLAM) algorithms have been an area of intense research. SLAM algorithms using only a laser scanner in an urban environment are accurate for ground vehicle navigation. Outdoor SLAM has been accomplished using a combination of vision and laser ranging data, which can increase the accuracy, at the cost of computational complexity. Actuated planar laser scanners have been shown to work in unstructured environments such as forests, but require extremely intensive computation, and are not suitable for real time navigation for aerial robots requiring precise height control.
Localizing robots in forestry and agricultural settings is an active area of research. Cameras are a popular option for guiding ground vehicles through row crops. Finding distinguishable textures and features in outdoor scenes have lead to viable localization algorithms, but in a fully grown agricultural field, the crops have very little visual contrast. In crops where the ground is exposed between rows, a ground based robot can use a camera to look down the rows and use the parallel row features to guide the robot. This approach is not practical in crops such as corn and soybeans, where the crops form a full canopy over the rows and visually obscure the parallel features. Also, a robot flying overhead may not have the correct perspective to make this type of approach work.
LiDARs frequently supplement or replace cameras for vehicle localization because of their high accuracy and ability to operate in variable and low light conditions. Hough transforms have been used to extract tree trunks from ground based vehicle LiDAR scans. Similarly, circular tree trunks have been extracted from LiDAR scans for forested environments. It may be possible for ground based vehicles to use similar approaches in corn fields, however the vehicle would damage crops. A UAV cannot use these approaches because the corn stalks are not well defined when view from above. Robots have been able to navigate through cluttered orchard environments with an a priori map and reflective tape, but this approach is not usable in large agricultural fields where the tape could only be placed at the perimeter of the field.